Keywords

1 Introduction

The recent pandemic has demonstrated the fragility of global healthcare systems, especially in Low-and Middle-Income countries (LMICs). The need to strengthen LMIC healthcare systems’ resilience is not a new concept, but has existed for a number of decades (Mabey 2004), summarized in 2009 in a seminal publication by the World Health Organization (WHO) on ‘Systems thinking for health systems strengthening’ (De Savigny and Adam 2009). This concept was iterated through the recent infectious disease outbreaks in LMICs in recent years, for example, during the Zika (Duchin 2016) and Ebola (Kluge et al. 2018; Kruk et al. 2015) virus outbreaks. However, it is important to note that within LMICs these infectious disease outbreaks exist in addition to other considerable infectious disease threats, namely malaria, tuberculosis (TB) and Human Immunodeficiency Virus (HIV) to cite a few (Hogan et al. 2020). Moreover, it has been predicted that a substantial increase of non-communicable diseases will also be anticipated in LMICs, for example, for cancer predicting a rise from ca. 05m deaths in 2020 to about 1m deaths by 2030 (Ngwa et al. 2022). Therefore, the development of digital health infrastructure has to address acute pressures, and more than likely be able to cover multiple overlapping needs within the existing healthcare systems.

Further pressures originate from changes in the nature of health care delivery itself (for example a greater shift on remote care), government and payer initiatives, the attitude of insurance organizations, consumer education and expectation, and rapid changes in technology. Altogether, the increasing needs and diverse pressures have prompted a push to consolidate biomedical laboratory analyses, where resources and services are centralized and serve a large(r) population for purposes of enhanced efficiency, increased standardization, and potentially earlier time to results (Vandenberg et al. 2020). This trend is universal and has been exhibited in high-income countries, as well as LMICs, though more intensely in the former (Mochon and Santa 2016).

Initial considerations included diagnostic costs, privatization, and scarcities of appropriately qualified personnel, as consolidating capacities could allow for technical scaling-up of offered services (Vandenberg et al. 2018). However, secondary benefits have also emerged, supported by the increasing digitalization, including for example integrated databases linked to regional/national reporting systems, as well as more easily managed biorepositories (Aisyah et al. 2023). The current prospective manuscript highlights the key needs of healthcare and medical research digitalization in LMICs relating to the digital health infrastructure.

2 Persistent Infrastructure Challenges

In high-income settings, consolidation of clinical services and lean approaches have been promoted for a number of years, in conjunction with clinical utility (Samuel and Novak-Weekley 2014; Miller et al. 2019). Similar initiatives were reported for LMICs; however, they were fewer by comparison (Shah et al. 2020; Micah et al. 2020). The challenges in the implementation of change, or even more of a systemic transformation, in LMIC healthcare are many and persistent. In the case of digitalization, they can be grouped in challenges relating to: infrastructure, equipment, consumables, human resources, available pathways to embed within routine healthcare, political priorities and governmental structures (Ombelet et al. 2018). A number of those will be specifically covered in subsequent chapters (e.g., Chap. 21 on the technical challenges; Chap. 23 on governance, etc.). The focus of the current chapter would be on the design of the digital health infrastructure so as to reflect current and future needs.

As highlighted previously, building capacity for clinical laboratories, for example, needs to include data management capacities, which currently remain under-resourced. For example, the lack of a fit-for-purpose and open-source laboratory information management system software is of particular concern, and has not been addressed even during the COVID-19 pandemic. Thus, without actions to improve information technology infrastructure and data management systems, ongoing efforts to develop capacity in LMICs are unlikely to realize their full potential (Turner et al. 2021). Furthermore, a digital technology or application transposed directly within and LMIC context may not operate as effectively as anticipated (by comparison to high-income contexts), as the local healthcare needs, clinical parameters (Alp and Rello 2019), and even relative abundance of infectious agents (Budayanti et al. 2020) may be different, hence exerting a distinctly local combination of needs and pressures. Moreover, emerging technologies and platforms are more easily assimilated in bigger laboratories, leaving the smaller ones (e.g., in peri-urban or rural settings) at risk of being left behind (Vandenberg et al. 2020). New and usually quite complex technologies already require (multiple) accreditation levels to comply with European Conformité Européenne (CE) or American Food and Drug Administration (FDA) guidelines, thus the implementation capacity within many LMIC settings is prohibitive.

3 Identifying the Key Structural Needs

As digital health interventions and electronic clinical decision support algorithms (CDSAs) in primary healthcare is identified by the WHO as key accelerators in achieving the 20,230 Sustainable Development Goal 3 of ensuring good health and wellbeing for all, digitalization should enable the emergence of small scale, cost-effective, semi-autonomous and decentralized clinical laboratories within LMICs.

Most large urban centers in LMICs have a healthcare structure that spans primary to tertiary healthcare facilities. While the capacity may be limited, and the local population under-served, the healthcare structure exists and has a blueprint for directional growth at each healthcare provision level (Nugraha et al. 2017; Massoud 2008). Digitalization can enable further expansion at the primary healthcare level, i.e., the entry point to healthcare for the majority of the population (Rawat et al. 2023). Smaller scale units can be created, operated by basically trained staff that are digitally connected with larger units that provide the support as and when required. This cost-effective approach has been implemented for some existing initiatives as a way of scaling-up (Bhattarai et al. 2022; Rodriguez-Villa et al. 2020). Such approaches can be adapted to accommodate inexpensive and robust techniques. The digitalization aspect will provide the necessary standardization of service provision and connectivity with other services, while the infrastructural focus will be centered on technical components such as accessibility to digital applications and to cloud/internet infrastructures. Of course, implementation of digitization initiatives between different tiers of healthcare is more complex than within the same tier (Eboreime et al. 2019). Thus, adequate pre-intervention planning, understanding, and engaging the various interests across the governance structures are key to improving the potential adoption and successful implementation.

The second key structural need for digital health infrastructure is the availability of ‘tropicalized’ equipment consumables and techniques, i.e., that would be able to operate within the technical challenges of LMICs without compromising the quality of the technical output (Tran 2016; Sankaran et al. 2010). This is process that can often be considered as ‘reverse-innovation’ or ‘bottom-up’ innovation (Trimble and Govindarajan 2012), where available core technologies are available on-site in LMICs, and undergo iterative rounds of co-design, adaptation and improvement so that aspects are optimized for the local operational contexts. In some cases, such a process can also result in the local production, disposal and/or distribution of resulting product variants (Naseri 2022; Sankaran et al. 2010). There are of course additional requirements, beyond the co-design process, such as the understanding of non-expert user and training requirements, so that the need for future technical support can be estimated. The use of digital health by non-expert users through the innovative smartphone algorithm using point-of-care testing at district hospital level has already demonstrated its added value in the clinical management of children suffering from febrile illnesses, in particular by improving the rational use of antibiotics (Keitel and D’Acremont 2018; Tan et al. 2023).

Finally, a key point is the need for field performance studies on LMICs for implementation of digitalization applications. The existing data, though very interesting, is incomplete and piecemeal within regions, reflecting the vertical programs driving such initiatives, as opposed to a broader healthcare system view (Keitel and D’Acremont 2018; Tan et al. 2023; Lazuardi et al. 2021). While individual solutions are unlikely to engage a wide adoption within an entire country/region, network-enabled solutions are more likely to succeed. Such field performance studies would address the ability of digitalization applications to meet end-users’ expectations by fulfilling ASSURED criteria (Affordable, Sensitive, Specific, User-friendly, Rapid and robust, Equipment-free and Deliverable to end-users), as recommended by the WHO (Tamrat et al. 2022; Luogaa et al. 2019). Another utility of field performance studies is the informing of policy makers regarding projected costs. For example, the integration within a universal healthcare service or a cycle of independently-funded low supply/low demand, represent only two of the many potential funding approaches that can be implemented. The availability of information is likely to lead towards a better-suited funding model for the digitalization approaches implemented.

4 Conclusions

The advent of digitization in healthcare is a universal phenomenon that has only been accelerated by the recent COVID-19 pandemic. LMIC settings face a unique complexity of healthcare challenges, where digital health infrastructure is likely to ameliorate at least part of the existing pressures. However, persistent infrastructure challenges provide a barrier to implementation (both adoption and diffusion) for many digitalization implementations. Therefore, key considerations have to be taken into account. These are the identification of key structural needs: firstly, the likely greater impact of digitalization in LMICs on primary healthcare, and as such the design of systems to support smaller, inter-connected units; secondly, the tropicalization of equipment, that can bely opportunities for co-development of digitalization applications under a universal health coverage system; and thirdly, the greater availability of field performance studies in LMICs, that would eventually inform future funding and support models. The digitalization of healthcare in LMICs is both a necessity and inevitability, however, the digitalization will be context-driven, and as such different implementation models are likely to emerge. Taking the key considerations above into account, such models can be further optimized to respond to the national/regional healthcare needs and pressures.